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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Application of the Semi-Supervised Learning Approach for Pavement Defect Detection.

Peng Cui^1,2, Nurjihan Ala Bidzikrillah¹, Jiancong Xu³

¹School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces a semi-supervised learning method using ResNet-18 to detect pavement defects from road images. Heatmap analysis improved classification accuracy for road surface quality monitoring.

Keywords:

ResNet-18 defect score explainability feature embedding vectors heatmap semi-supervised

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Area of Science:

Computer Science
Civil Engineering
Materials Science

Background:

Road surface quality is critical for driver safety and comfort.
Real-time detection of pavement defects is challenging due to defect diversity and complex environmental conditions.

Purpose of the Study:

To develop a robust algorithm for classifying and detecting pavement defects.
To improve road surface quality monitoring using semi-supervised learning and image analysis.

Main Methods:

Employed a semi-supervised learning approach with ResNet-18 for image feature extraction.
Utilized one-class classification with a multivariate normal distribution model for normal pavement images.
Applied Mahalanobis distance for defect detection and receiver operating characteristic curves for threshold calibration.

Main Results:

The model achieved improved classification accuracy from 0.868 to 0.887 with expanded and augmented data.
Heatmaps provided insights into network decisions, guiding performance improvements.
The Mahalanobis distance effectively distinguished between normal and defective pavement images.

Conclusions:

Semi-supervised learning combined with ResNet-18 offers a promising approach for pavement defect detection.
Heatmap visualization aids in understanding and enhancing the performance of defect detection models.
Accurate road surface monitoring can be achieved through advanced image analysis techniques.